Scaling of SNP coefficients when simulating phenotypes
When developing a new statistical method in the context of genetic association testing, it’s common to simulate phenotypes on which to test the new method. This generally involves the following steps:
 Taking real genotype data (or simulating some from scratch, for example with msprime);
 Choosing a set of genetic variants which will be used to build the phenotype;
 Simulating coefficients for these genetic variants; and
 Adding noise to achieve a desired level of heritability.
This post will focus on step 3, that of simulating coefficients for the variants (SNPs) chosen. More specifically, we will consider the question of whether and how perSNP heritability should vary with the SNP’s minor allele frequency (MAF).
1. The problem: perSNP heritability increases with MAF
Suppose we have chosen our set of SNPs in step 2 and now want to simulate their coefficients. A simple approach would be to simply draw them independently from a standard Normal distribution (mean zero, variance one). The problem with this approach is that it ignores an important piece of information about each SNP: their minor allele frequency (MAF).
In real populations, some mutations are more common than others. Moreover, because of natural selection, more common mutations generally have smaller effect sizes that rarer mutations. This has been observed empirically in GWAS (see for example this post by Sasha Gusev on Twitter) and can be explained theoretically by a model in which a trait is assumed to be at its optimal value in the population – then, any mutations that arise will be harmful and so the only mutations which are allowed to increase in frequency and become common are those with small effect sizes.
Since this inverse relationship between allele frequency and effect size is a key feature of realworld assocations between genotypes and phenotypes, it’s important that we mimic it in our simulations. Let’s now compute the perSNP heritability (that is, the expected contribution of each SNP to the variance of the trait) to examine what this relationship looks like if we simply simulate coefficients from independent standard Normals.
We can compute the perSNP heritability as follows:
\[\begin{align*} \text{Var}(\beta_j x_j) &= \text{Var}(\beta_j) \text{Var}(x_j) + \text{Var}(\beta_j) {\left[\mathbb{E}(x_j)\right]}^2 + {\left[\mathbb{E}(\beta_j)\right]}^2 \text{Var}(x_j). \end{align*}\]Since \(\mathbb{E}(\beta_j) = 0\), the final term goes away. And by meancentring the genotypes (which we haven’t done, but just for simplicity here) the second term is also zero. We continue:
\[\begin{align} \text{Var}(\beta_j x_j) &= \underbrace{\text{Var}(\beta_j)}_{=1} \text{Var}(x_j) \label{eq:varbx} \tag{1}\\ &= 2p_j(1p_j), \nonumber \end{align}\]where \(p_j\) is the minor allele frequency of \(x_j\), since \(x_j\) is binomial.
This implies that the heritability explained by each SNP varies across SNPs and increases linearly with the MAF (i.e., more common SNPs explain more heritability on average).
Since we expect rarer SNPs to have larger effect sizes (so a larger value for \(\text{Var}(\beta_j)\)), we would like the linear, increasing relationship between heritability and frequency above to actually be flat (i.e., all SNPs explain the same amount of heritability in expectation) or at least less pronounced (as it is, we have a onetoone relationship between MAF and heritability explained).
This leads us to the most common approach to simulate coefficients, which is to make them have constant perSNP heritability regardless of frequency.
2. The most common approach: constant perSNP heritability
The most common way to simulate coefficients when building artificial phenotypes is to make them have a fixed contribution to heritability which doesn’t depend on the MAF. This can be achieved in several ways.
2.1 Scaling the variance of the Normal distribution
We can draw our coefficients from a Normal distribution whose variance is scaled by the reciprocal of the variance of the corresponding genotype:
\[\begin{align*} \beta_j \sim \text{Normal}\left(0, \frac{1}{2p_j(1p_j)}\right). \end{align*}\]Equation \eqref{eq:varbx} then becomes:
\[\begin{align*} \text{Var}(\beta_j x_j) &= \frac{1}{2p_j(1p_j)} 2p_j(1p_j) = 1, \end{align*}\]achieving constant heritability explained across SNPs regardless of MAF.
This is the approach used for the simulations in the LD Score regression paper,^{1} in which perallele effect sizes are drawn from a \(\text{Normal}(0, h^2 / (2Mp_j(1p_j)))\) distribution, where \(h^2\) is the desired level of heritability^{2} and \(M\) the number of causal SNPs.
2.2 Drawing the coefficients from a standard Normal and then rescaling them
An alternative and equivalent way to obtain a fixed heritability per SNP is to first draw the coefficients from a standard normal as in section 1 and then rescale them by the square root of the reciprocal of the variance. This amounts to replacing the coefficients \(\beta_j\), drawn from a standard Normal, with scaled coefficients \(\delta_j\):
\[\begin{align*} \delta_j = \frac{1}{\sqrt{2p_j(1p_j)}} \beta_j. \end{align*}\]We can then write:
\[\begin{align*} \text{Var}(\delta_j x_j) &= \text{Var}\left(\frac{1}{\sqrt{2p_j(1p_j)}}\beta_j\right) \text{Var}(x_j)\\ &= \frac{1}{2p_j(1p_j)}2p_j(1p_j) = 1. \end{align*}\]This is the setup used in the LDpredfunct paper,^{3} which incorporates functional priors to improve the accuracy of LDPred.
2.3 Drawing the coefficients from a standard Normal and normalising the genotypes instead
Finally, we can achieve the same result by simply normalising the genotypes (i.e., dividing them by \(\sqrt{2p_j(1p_j)}\)) while still drawing our coefficients from a standard Normal. Writing \(g_j = x_j / \sqrt{2p_j(1p_j)}\) as the standardised genotypes, equation \eqref{eq:varbx} now becomes:
\[\begin{align*} \text{Var}(\beta_j g_j) &= \text{Var}(\beta_j) \text{Var}\left(\frac{x_j}{\sqrt{2p_j(1p_j)}}\right)\\ &= \frac{1_j}{2p_j(1p_j)} \cdot \underbrace{\text{Var}(x_j)}_{=2p_j(1p_j)} = 1. \end{align*}\]This approach seems the most common of the three and is used, for example, in the GCTA paper,^{4} in the BOLTLMM paper^{5} and in the REGENIE paper.^{6}
3. A more realistic approach: a scaled relationship between heritability and MAF
More recently, Doug Speed et al. proposed the LDAK model under which the expected (relative) heritability contribution of each SNP is given by the following formula:^{7}
\[\begin{align} \mathbb{E}(h_j^2) \propto {[f_j(1f_j)]}^{1+\alpha} \cdot w_j r_j, \label{eq:ldak} \tag{2} \end{align}\]where the relationship between heritability and the MAF is controlled by the parameter \(\alpha\), \(w_j\) is a SNP weight computed based on local levels of LD (it’s higher for SNPs in regions of low LD) and \(r_j \in [0, 1]\) is an information score measuring confidence in a SNPs imputation (\(r_j\) is 1 for directly genotyped SNPs and is otherwise an estimate of the squared correlation between the true and imputed genotypes).
3.1 Deriving the heritability expression
Equation \eqref{eq:ldak} arises from the following model for the phenotype \(Y_i\):
\[\begin{align*} Y_i = \sum_k \theta_k Z_{ik} + \sum_j \beta_j X_{ij} + e_i, \end{align*}\]with \(\beta_j \sim \text{Normal}(0, r_j w_j\sigma_g^2 / W)\), \(e_i \sim \text{Normal}(0, \sigma_e^2)\) and \(W = \sum_j r_j w_j [2f_j(1f_j)]^{1 + \alpha}\). The variables \(Z_{ik}\) are fixedeffect covariates and the \(X_{ij}\) are the centred and scaled allele counts (i.e., \(X_{ij} = (S_{ij}  2f_j) \cdot {[2f_j(1f_j)]}^{\alpha/2}\), with \(S_{ij}\) being the raw allele count).
Under this model, we can derive the expected (relative) heritability of SNP \(j\) as follows:
\[\begin{align*} \mathbb{E}(h_j^2) &= \frac{\text{Var}(\beta_jX_j)}{\text{Var}(Y)} \\ &= \frac{\mathbb{E}(\beta_j^2) \text{Var}(X_j)}{\text{Var}(Y)}\\ &= \frac{r_jw_j\sigma_g^2/W \cdot [2f_j(1f_j)]^\alpha [2f_j(1f_j)]}{\text{Var}(Y)}\\ &= \frac{r_jw_j\sigma_g^2/W \cdot [2f_j(1f_j)]^{1 + \alpha}}{\text{Var}(Y)}. \end{align*}\]Since \(\sigma_g^2\), \(W\) and \(\text{Var}(Y)\) are the same for all SNPs, we can consider them scaling constants and so obtain equation \eqref{eq:ldak}.
3.2 Relation to previous approaches
Modelling the contribution of imputation quality (through \(r_j\)) and the levels of local LD (through \(w_j\)) is new and so does not fit within the framework of section 2. If we ignore these two factors (and so set \(r_j = w_j = 1\)), we see that Speed et al.’s approach is equivalent to the constant heritability model for \(\alpha = 1\).
In their paper, Speed et al. fitted their model to data for 42 quantitative traits assuming different values for \(\alpha\) (\(1.25, 1, 0.75, 0.5, 0.25, 0\) and \(0.25\)) to determine which value led to the best model fit (likelihood). They found \(\alpha = 0.25\) to provide the best fit overall, although a slightly better fit could be achieved for some traits with other values.
With \(\alpha = 0.25\), equation \eqref{eq:ldak} becomes:
\[\begin{align*} \mathbb{E}(h_j^2) \propto {[f_j(1f_j)]}^{0.75} \cdot w_j r_j. \end{align*}\]As can be seen in the figure below, this implies a positive relationship between MAF and perSNP heritability which grows more slowly as the MAF rises.
Relationship between heritability and MAF (Fig. 2a from Speed et al.)
Caption in the paper: 'The parameter \(\alpha\) specifies the assumed relationship between heritability and MAF: in human genetics, \(\alpha = 1\) is typically used (solid blue line), while in animal and plant genetics \(\alpha = 0\) is more common (orange); we instead found that \(\alpha = 0.25\) (red) provides a better fit to real data. The gray bars report (relative) estimates of the perSNP heritability for SNPs with \(\text{MAF} < 0.1\) and \(\text{MAF} \geq 0.1\), averaged across the 19 GWAS traits (vertical lines provide 95% confidence intervals); the dashed lines indicate the perSNP heritability predicted by each \(\alpha\) value.'
Comparing this setup to the simplest approach in section 1, we see that they are similar, with only a slightly smaller coefficient here. The two approaches would be equivalent for \(\alpha=0\), again ignoring \(r_j\) and \(w_j\).
4. Which approach should I use?
As mentioned above, the second approach of making heritability independent of MAF (equivalent to setting \(\alpha = 1\) in the LDAK framework) is the most commonly used in practice. However, in light of Speed et al.’s more recent empirical analysis, it may make sense to build phenotypes using different values for \(\alpha\) to ensure the simulated scenarios cover a range of plausible architectures.
This is the approach taken by Brian Zhang et al. in their recent paper proposing ARGNeedle, a new method to infer ancestral recombination graphs from large samples. In their simulations, they set \(\alpha \in \{0, 0.5, 1\}\) (see Figure S6 in the Supplementary Material) thus covering the two scenarios in sections 1 and a 2 as well as an intermediate setting.
5. Summary
We have seen how the naive approach of simply simulating coefficients from identical distributions that don’t take their allele frequency into account leads to more common SNPs contributing disproportionately more to the heritability of a trait (i.e., expected heritability explained increases onetoone with allele frequency).
We then saw how the common alternative to this setup in simulation studies in the literature is to have the expected heritability explained be constant regardless of MAF, and the multiple ways in which this can be achieved.
Lastly, we considered a newer model in which heritability does increase with frequency but in a less pronounced way. This model appears to be favoured by empirical data.
Going forward, one could consider building different simulation settings for multiple values of \(\alpha\) in the LDAK framework as some researchers in the community have already started doing!

See section titled Simulations with polygenic genetic architectures in the main text. ↩

By drawing the residuals from a distribution with variance \((1  h^2)\) – see Section 1.1 of the Supplementary Note, although it’s a theory section and doesn’t refer to the simulations – the SNP heritability under this setup is \(h^2 / (h^2 + 1  h^2) = h^2\). ↩

See section titled Simulations in the main text. ↩

See section titled GWAS Simulation in the main text. ↩

See section 5.1.2 Simulated phenotypes in the Supplementary Text. ↩

See section titled Data simulation in the main text. ↩

We adopt the paper’s notation and now denote the MAF by \(f_j\). ↩